The efficiency of information gain by natural selection
8 affiliations
- 1Department of Neuroscience, Washington University School of Medicine in St. Louis
- 2Deprtment of Physics, Wshington University in St. Louis
- 3Department of Biological Sciences, University of Idaho
- 4Department of Biology, University of Washington
- 5Department of Information and Computing Sciences, Utrecht University
- 6Department of Mathematics, Utrecht University
- 7Department of Biology, University of Pennsylvania
- 8Santa Fe Institute
Abstract
Natural selection enriches populations for genotypes that are well adapted to their environments, accumulating information about the selective conditions in the process. Therefore, selection can be viewed as a learning process through which populations acquire information about the environment. Motoo Kimura derived a relationship between the amount of information gained by selection and a measure of the inefficiency of the selective process, known as substitution load [M. Kimura, Genet. Res. 2, 127–140 (1961)]. Due to the gradual nature of selection and the ongoing mismatch of types to the environment, a lineage that is still acquiring information has lower mean fitness than a lineage that already has it. While this has been an influential insight, we find that Kimura’s model is inconsistent with selection experiments involving Escherichia coli . To address limitations in this model and develop a more robust measure for the efficiency of selection, we turn to computational learning theory. We show that the learning-theoretic concept of regret quantifies the inefficiency of selection as a learning process in terms of relative lineage fitness. We establish bounds on the efficiency of information gain in general settings, including frequency-dependent selection and arbitrary environmental change. We experimentally validate these bounds and demonstrate that our extended theory recovers Kimura’s fundamental insight. Finally, we show that selection is an asymptotically optimal algorithm for the basic problem faced by evolving populations and that it acquires information more efficiently than comparable processes in complex environments.
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BibTeX
@article{mcgee2026efficiency,
title = {{The efficiency of information gain by natural selection}},
author = {Ryan Seamus McGee and Olivia Kosterlitz and Artem Kaznatcheev and Benjamin Kerr and Carl T. Bergstrom},
journal = {Proceedings of the National Academy of Sciences},
year = {2026},
month = oct,
doi = {10.1073/pnas.2611968123},
url = {https://doi.org/10.1073/pnas.2611968123},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - The efficiency of information gain by natural selection
AU - Ryan Seamus McGee
AU - Olivia Kosterlitz
AU - Artem Kaznatcheev
AU - Benjamin Kerr
AU - Carl T. Bergstrom
PY - 2026
DA - 2026/10/29
JO - Proceedings of the National Academy of Sciences
DO - 10.1073/pnas.2611968123
UR - https://doi.org/10.1073/pnas.2611968123
AB - Natural selection enriches populations for genotypes that are well adapted to their environments, accumulating information about the selective conditions in the process. Therefore, selection can be viewed as a learning process through which populations acquire information about the environment. Motoo Kimura derived a relationship between the amount of information gained by selection and a measure of the inefficiency of the selective process, known as substitution load [M. Kimura, Genet. Res. 2, 127–140 (1961)]. Due to the gradual nature of selection and the ongoing mismatch of types to the environment, a lineage that is still acquiring information has lower mean fitness than a lineage that already has it. While this has been an influential insight, we find that Kimura’s model is inconsistent with selection experiments involving Escherichia coli . To address limitations in this model and develop a more robust measure for the efficiency of selection, we turn to computational learning theory. We show that the learning-theoretic concept of regret quantifies the inefficiency of selection as a learning process in terms of relative lineage fitness. We establish bounds on the efficiency of information gain in general settings, including frequency-dependent selection and arbitrary environmental change. We experimentally validate these bounds and demonstrate that our extended theory recovers Kimura’s fundamental insight. Finally, we show that selection is an asymptotically optimal algorithm for the basic problem faced by evolving populations and that it acquires information more efficiently than comparable processes in complex environments.
SN - 0027-8424
SN - 1091-6490
ER -